filename=data_dir + cell + "_" + str(wid) + "bp_Classes.txt";
classes=numpy.loadtxt(filename,delimiter='\t',dtype=object)
filename=data_dir+ cell + "_Features.txt"
features=numpy.loadtxt(filename,delimiter='\t',dtype=object)
given=["A-E","I-E","A-P","I-P","A-X","I-X","UK"]
#given=["A-E","I-E"]
#given=["A-P","I-P"]
#given=["A-E","A-P"]
#given=["A-E","A-X"]
#given=["A-P","A-X"]
#given=["A-E","A-P","A-X"]
#given=["A-E","I-E","A-P","I-P"]
#given=["A-E","I-E","A-P","I-P","A-X","I-X"]
#given=["I-E","I-P"]
data,classes,_=cl.take_some_classes(data,classes,given=given,others=None)
# balance the sample sizes of the classes
rng=numpy.random.RandomState(1000)
data,classes,others=cl.balance_sample_size(data,classes,others=None,min_size_given=None,rng=rng)
print data.shape
print numpy.unique(classes)
#group=[["A-E"],["I-E"],["A-P"],["I-P"],["A-X"],["I-X"],["UK"]]
#group=[["A-E","A-P"],["I-E","I-P","A-X","I-X","UK"]]
#group=[["A-E","A-P","A-X"],["I-E","I-P","I-X","UK"]]
group=[["A-E"],["A-P"],["I-E","I-P","A-X","I-X","UK"]]
#group=[["A-E"],["A-P"],["A-X"],["I-E","I-P","I-X","UK"]]
#group=[["A-E"],["I-E"]]
#group=[["A-P"],["I-P"]]
Each row is a string (white space not allowed) as the feature name of the corresponding column in [1].
"""
##################################
#load your data here ...
##################################
filename="/home/yifengli/research/dnashape/result/Data_1000bp.txt";
data=numpy.loadtxt(filename,delimiter='\t',dtype='float16')
filename="/home/yifengli/research/dnashape/result/Classes_1000bp.txt";
classes=numpy.loadtxt(filename,delimiter='\t',dtype=str)
filename="/home/yifengli/research/dnashape/result/Features.txt";
features=numpy.loadtxt(filename,delimiter='\t',dtype=str)
# change class labels
given=["Enhancer","EnhancerFalse"]
data,classes=cl.take_some_classes(data,classes,given)
given={"Enhancer":0,"EnhancerFalse":1}
classes=cl.change_class_labels_to_given(classes,given)
train_set_x_org,train_set_y_org,valid_set_x_org,valid_set_y_org,test_set_x_org,test_set_y_org \
=cl.partition_train_valid_test(data,classes,ratio=(1,1,1))
del data
gc_collect()
rng=numpy.random.RandomState(1000)
numpy.warnings.filterwarnings('ignore')
# train
classifier,training_time=convolutional_mlp.train_model( train_set_x_org=train_set_x_org, train_set_y_org=train_set_y_org,
valid_set_x_org=valid_set_x_org, valid_set_y_org=valid_set_y_org,
n_row_each_sample=4,
classes = numpy.loadtxt(filename, delimiter='\t', dtype=object)
filename = data_dir + cell + "_Features.txt"
features = numpy.loadtxt(filename, delimiter='\t', dtype=object)
given = ["A-E", "I-E", "A-P", "I-P", "A-X", "I-X", "UK"]
#given=["A-E","I-E"]
#given=["A-P","I-P"]
#given=["A-E","A-P"]
#given=["A-E","A-X"]
#given=["A-P","A-X"]
#given=["A-E","A-P","A-X"]
#given=["A-E","I-E","A-P","I-P"]
#given=["A-E","I-E","A-P","I-P","A-X","I-X"]
#given=["I-E","I-P"]
data, classes, _ = cl.take_some_classes(data,
classes,
given=given,
others=None)
# balance the sample sizes of the classes
rng = numpy.random.RandomState(1000)
data, classes, others = cl.balance_sample_size(data,
classes,
others=None,
min_size_given=None,
rng=rng)
print data.shape
print numpy.unique(classes)
#group=[["A-E"],["I-E"],["A-P"],["I-P"],["A-X"],["I-X"],["UK"]]
#group=[["A-E","A-P"],["I-E","I-P","A-X","I-X","UK"]]
Each row is a string (white space not allowed) as the feature name of the corresponding column in [1].
"""
##################################
#load your data here ...
##################################
filename = "/home/yifengli/research/dnashape/result/Data_1000bp.txt"
data = numpy.loadtxt(filename, delimiter='\t', dtype='float16')
filename = "/home/yifengli/research/dnashape/result/Classes_1000bp.txt"
classes = numpy.loadtxt(filename, delimiter='\t', dtype=str)
filename = "/home/yifengli/research/dnashape/result/Features.txt"
features = numpy.loadtxt(filename, delimiter='\t', dtype=str)
# change class labels
given = ["Enhancer", "EnhancerFalse"]
data, classes = cl.take_some_classes(data, classes, given)
given = {"Enhancer": 0, "EnhancerFalse": 1}
classes = cl.change_class_labels_to_given(classes, given)
train_set_x_org,train_set_y_org,valid_set_x_org,valid_set_y_org,test_set_x_org,test_set_y_org \
=cl.partition_train_valid_test(data,classes,ratio=(1,1,1))
del data
gc_collect()
rng = numpy.random.RandomState(1000)
numpy.warnings.filterwarnings('ignore')
# train
classifier, training_time = convolutional_mlp.train_model(
train_set_x_org=train_set_x_org,
train_set_y_org=train_set_y_org,
